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https://github.com/mongodb-developer/whatscooking-multilingual

whatscooking multilingual
https://github.com/mongodb-developer/whatscooking-multilingual

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whatscooking multilingual

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README 

        
What's Cooking with Atlas Search



A Restaurant Finder Application Demo MongoDB Atlas Search


Hello! 👋 This application allows you to search lightning fast through restaurants in English, Thai, Indonesian and Japanese based on a variety of search parameters and data types:


    

  • restaurant name
    • 

  • food name
    • 

  • geolocation coordinates
    • 

  • cuisine type
    • 

  • average star rating
    • 

  • borough
    • 















Note1: This dataset is mocked. Please do not use to make actual dining decisions.


Note2: This demo is multiligual version of the What's Cooking repos.




 What's Cooking implements many Atlas Search features from autocomplete to custom function scoring. Using the $search operator in a MongoDB aggregation pipeline, we can build fine-grained searches across text, numerics, and geospatial data. By building out What's Cooking, you'll learn all sorts of ways MongoDB allows you to build complex, fine-grained full-text searches on your Atlas data.



**No additional servers or software needed. No need to keep data in sync. Everything is done in MongoDB Atlas.**



- fuzzy matching

- highlighting

- autocomplete

- range queries

- geoqueries

- facets

- relevance-based scoring

- custom function scoring

- synonyms



Check out the demo video to see a demonstration of all the features or visit the link below to play around with the finished application, hosted entirely in MongoDB Atlas:


whatscooking.mongosa.net



This application is hosted entirely by MongoDB Atlas was created using:



- React

- Tailwind CSS

- MongoDB Realm for backend HTTPs endpoints and functions

- A modified sample dataset based on MongoDB's Atlas sample_restaurants dataset





    




Currently this app is not suitable for mobile, but feel free to send a PR. 😊



Prerequisites



- A MongoDB Atlas account. Get one for free here.

- Node.js version 16 and npm.

- Restaurant sample dataset.

- Synonyms dataset.

- (Recommended) MongoDB Compass - GUI



You can read and download the dataset using the MongoDB Shell, any MongoDB driver, or my favorite MongoDB Compass using the following URI:




  

mongodb+srv://mongodb:[email protected]/whatscooking






It is also included in this repo's data and indexes directory.



---



To Build This Application...



Prepare Data



    

  1.  Load data to Atlas cluster:

      

    • database: whatscooking

      • 

    • collection: restaurants_[locale] (e.g. restaurants_en), menu_synonyms_[locale](e.g. menu_synonyms_en)

      • 

    

    2. 


  2.  Create Search indexes. (Index definitions includes in indexes directory.)
    3. 

      

    • default
      • 

    • autocomplete
      • 

    • facetIndex
      • 

    




 Deploy HTTPS Endpoints at your own AWS environment



Follow the instructions in [Whatscooking BaaS using AWS Lambda Functions](./lambda/README.md)



 Testing HTTPS endpoints


You can test each HTTPS endpoints with the following commands

getRestaurantsAutocomplete


command


curl https://7voovjzrkjgdudds53so7rtsae0vjouf.lambda-url.ap-southeast-1.on.aws?restname=burger&locale=en



response


[{"_id":"6095a34a7c34416a90d3212d","name":"Burger King","restaurant_id":"40370238"},{"_id":"6095a34a7c34416a90d3212e","name":"Burger King","restaurant_id":"40370167"},{"_id":"6095a34a7c34416a90d32135","name":"Burger King","restaurant_id":"40370239"},{"_id":"6095a34a7c34416a90d3214b","name":"Burger King","restaurant_id":"40370916"},{"_id":"6095a34a7c34416a90d32164","name":"Burger King","restaurant_id":"40370917"},{"_id":"6095a34a7c34416a90d32166","name":"Burger King","restaurant_id":"40372422"},{"_id":"6095a34a7c34416a90d3216b","name":"Burger King","restaurant_id":"40372618"},{"_id":"6095a34a7c34416a90d321b4","name":"Cozy Soup \u0026 Burger","restaurant_id":"40375839"},{"_id":"6095a34a7c34416a90d3228a","name":"Burger Barn Restaurant","restaurant_id":"40384486"}]



getRestaurants


command


curl \

-H "Content-Type: application/json" \

-d '{"searchTerm": "burger ", "food": "", "operator": "text",    "dist": 1, "stars": 1, "cuisine": [], "locale": "en"}' \

https://f6lhyweuc6xvqp5jylghwt7v5u0qzwjz.lambda-url.ap-southeast-1.on.aws 






response


{"aggString":"[{\"$search\":{\"text\":{\"query\":\"burger \",\"path\":\"name\",\"fuzzy\":{\"maxEdits\":2}}}},{\"$limit\":21},{\"$project\":{\"name\":1,\"cuisine\":1,\"borough\":1,\"location\":1,\"menu\":1,\"restaurant_id\":1,\"address.street\":1,\"stars\":1,\"review_count\":1,\"PriceRange\":1,\"sponsored\":1,\"score\":{\"$meta\":\"searchScore\"},\"highlights\":{\"$meta\":\"searchHighlights\"}}}]","restaurants":[{"_id":"6095a4864ba3a04a69a79eba","address":{"street":"Pearl Street"},"borough":"Manhattan","cuisine":"Hamburgers","name":"Burger Burger","restaurant_id":"41316784","location":{"type":"Point","coordinates":[-74.0105051,40.7040805]},"stars":3.5,"review_count":159,"menu":["Bacon burger","Santa Fe burger","Ahi Tuna burger","Cheeseburger","Loaded Fries","Mushroom swiss burger","Hickory burger","Classic burger","Fajita burger","Oldtimer with cheese","French Fries","Vegetarian burger"],"PriceRange":2,"score":3.3074374198913574},{"_id":"6095a34a7c34416a90d3212d","address":{"street":"Northern Boulevard"},"borough":"Queens","cuisine":"Hamburgers","name":"Burger King","restaurant_id":"40370238","location":{"type":"Point","coordinates":[-73.89707140000002,40.7543896]},"stars":3,"review_count":38,"menu":["Cheeseburger","Ahi Tuna burger","Chili Cheeseburger","Buffalo Fries","Vegetarian burger","Loaded Fries","French Fries","Classic burger","Triple layer burger","Oldtimer with cheese","Hickory burger","Oldtimer"],"PriceRange":2,"score":2.49027419090271}, ...}




getFacets


command


curl \

-H "Content-Type: application/json" \

-d '{"searchTerm": "burger", "food": "", "operator": "text", "dist": 1, "stars": 1, "cuisine": [], "locale": "en"}' \

https://cwoaaqy74pwe76cajhevu7kaby0eutbe.lambda-url.ap-southeast-1.on.aws




response


{"results":[{"count":{"lowerBound":183},"facet":{"cuisineFacet":{"buckets":[{"_id":"Hamburgers","count":105},{"_id":"American","count":69},{"_id":"Other","count":3},{"_id":"Jewish/Kosher","count":2},{"_id":"Pizza/Italian","count":2},{"_id":"Latin (Cuban, Dominican, Puerto Rican, South \u0026 Central American)","count":1},{"_id":"Mexican","count":1}]},"boroughFacet":{"buckets":[{"_id":"Manhattan","count":69},{"_id":"Brooklyn","count":47},{"_id":"Queens","count":36},{"_id":"Bronx","count":23},{"_id":"Staten Island","count":8}]}}}],"searchMetaStageString":"{\"$searchMeta\":{\"index\":\"facetIndex\",\"facet\":{\"operator\":{\"text\":{\"query\":\"burger\",\"path\":[\"name\",\"cuisine\"]}},\"facets\":{\"cuisineFacet\":{\"type\":\"string\",\"path\":\"cuisine\"},\"boroughFacet\":{\"type\":\"string\",\"path\":\"borough\"}}}}}","searchMetaStage":{"$searchMeta":{"index":"facetIndex","facet":{"operator":{"text":{"query":"burger","path":["name","cuisine"]}},"facets":{"cuisineFacet":{"type":"string","path":"cuisine"},"boroughFacet":{"type":"string","path":"borough"}}}}},"ok":true}



getFoodSynonyms


command


curl https://nqez7rq6c6hkbsydjsf3ktpfru0qkaan.lambda-url.ap-southeast-1.on.aws?locale=en




response


{"foodSynonyms":[{"_id":"6268a01b5899f60be615cb66","input":["noodles"],"mappingType":"explicit","synonyms":["lo mein","chow mein","pasta","udon","ramen","spaghetti","alphabetti","macaroni","pasta"],"date_inserted":"2022-04-27T01:44:59.057Z","editable":false}],"ok":true}




To Run This Application....



1. Clone the repo.

2. Navigate inside `whatscooking-multilingual` directory.

3. Run npm install .

4. Change HTTPS endpoint urls at src/hooks/useHomeFetch.js to your own ones.

5. Change HTTPS endpoint urls at src/compenents/SearchBar.js to your own one.

6. Change MongoDB Connectionstring at src/pages/IndexPage.js to your own one.

7. Run npm start  .



To Deploy Frontend to AWS S3...



1. Make sure that you're using NodeJS v16 to successfully build the artifacts. If you're using [nvm](https://github.com/nvm-sh/nvm) to manage your Node version, just type `nvm use` to swich to the correct NodeJS version

2. Set AWS temporary credentials in your environment variable and ensure that you're pointing to the correct AWS account

3. Run ./deploy_to_s3.sh

4. The S3 bucket is fronted by CloudFront, so it might take a while for the cache to be refreshed. If you want [invalidate the cache manually](https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/Invalidation.html), following the instructions when you run the script



---

React Components....





    




Using AWS Lambda as Your Serverless Backend....


What's Cooking uses AWS Lambda Functions to create 5 APIs to allow you to query for your restaurant data over HTTP: 



- `GetRestaurantsEndPoint` called from the `useHomeFetch.js` hook.

- `GetFacetsEndpoint` called from the `useHomeFetch.js` hook.

- `Suggestions_AC_Endpoint` called from the `SearchBar.js` component.

- `getSynonyms` called in the `SynonymsPage.js`.



    







    







    







    






If you have any questions or feedback about this repo, feel free to create an Issue or PR in this repo.



Also please join our online MongoDB Community to interact with our product and engineering teams along with thousands of other MongoDB and Realm users. 

Have fun and happy coding!